The human and the machine: an actor-network theory analysis of artificial intelligence’s role in the tourism and hospitality ecosystems

Purpose This study examines how guests interact with artificial intelligence (AI) technologies in tourism and hospitality settings using actor-network theory (ANT), addressing critical gaps in understanding AI adoption patterns, variations in guest satisfaction and the formation of stable human-technology networks across different traveler segments and service touchpoints. Design/methodology/approach The research analyzed 20,000 TripAdvisor guest reviews from January 2023 to December 2024 using a mixed-methods approach. Qualitative thematic analysis via NVivo 14 identified 13 distinct technology themes, while python-based natural language processing employed VADER sentiment analysis. Findings AI contactless payments (88% adoption) and digital keys (82% adoption) demonstrated stable actor-networks, while AI chatbots showed critical instability with 48% negative sentiment and declining trust (3.8/10). Hybrid human–AI service channels achieved the highest satisfaction (8.4/10) compared to fully automated systems. Traveler preferences varied considerably, from 92% AI preference among tech enthusiasts to 18% among seniors. Critical unmet needs emerged, including luggage tracking (9.1 pain level) and discovery of authentic experiences (8.5 pain level), representing opportunities for AI intervention. Originality/value This study offers a novel empirical application of ANT, supported by large-scale review analytics, operationalizing its core constructs of translation and enrollment. It provides empirical evidence of task-specific AI agency in tourism and hospitality, showing guests grant agency to invisible AI enhancements while resisting conversational AI replacements for human service.